Discovery Call

Book a Discovery Call

Book a discovery call when a reporting, revenue, data, workflow, measurement, or operating decision is stuck and the right next step is not yet clear.

You do not need a polished brief. Bring the version of the problem that is slowing decisions down right now.

60% → 95%

Attribution coverage improved for a mid-market SaaS team after the reporting logic was rebuilt around revenue reality.

99%+

Pipeline uptime achieved after replacing brittle transformations with a tested dbt foundation.

18%

Churn reduction achieved in three weeks when warehouse data was operationalized into a real workflow.

What to expect on the call

The decision that is stuck

We spend the 30 minutes on the decision that is blocked: which number is not trusted, which reporting or workflow problem keeps returning, or which build cannot be scoped responsibly yet.

The right starting point

You do not need to choose a package first. We will separate a focused diagnostic or translation need from a core service engagement, a larger build, or a specialized AI-readiness question.

A practical next step

If there is a fit, you leave with the clearest next move — a focused diagnostic, translation sprint, service engagement, scoped build, optional run path, or a recommendation not to overcomplicate the problem yet.

This call is most useful when...

  • a senior VP or director owns a reporting, revenue, data, or workflow decision that cannot keep waiting
  • a CEO or small leadership team needs one defensible view of channel performance, pipeline quality, product motion, or revenue
  • marketing, finance, product, RevOps, and data teams are defending different versions of the same number or operating rule
  • an AI strategy owner has a specific proposed use case and is not convinced the source data, metric definitions, or workflow ownership are ready

If the problem is smaller than a consulting engagement, that is still a useful outcome. A clear "not yet" is better than forcing a project.

Choose a time

Pick a slot that works. If you would rather send context first, email [email protected].

Before you book

Use the booking notes, or send a short email to [email protected], with the line that best matches why you are reaching out:

  • Focused diagnostic or translation sprint: one narrow trust question needs a practical read, or an urgent business ask needs a scope the team can execute.
  • Core service engagement: the problem is already clear enough to address through revenue analytics, attribution, RevOps, fractional analytics, Data Foundation, or Data Activation.
  • Larger build: you need to certify metrics, repair a foundation, activate a model or workflow, or prove lift across a material decision.
  • Specialized AI-Ready Data Diagnostic: the specific question is whether source trust, governed metric definitions, entity resolution, and workflow ownership can support a proposed AI use case. See the AI-Ready Data Diagnostic.

Managed Run + Measure context

If you are asking about Managed Run + Measure, something should already be live and worth protecting. Include what is running, who owns it, what needs monitoring, the leadership or workflow cadence it supports, and what would break if it drifted.

The kinds of outcomes these conversations usually unlock

Not vanity quotes. These are the kinds of business outcomes that happen when the underlying data problem gets named correctly and fixed in the right order.

Names are withheld here because these conversations often start before a client wants public attribution, but each example below maps to a published case study so you can see the kind of work behind the outcome.

60% → 95% attribution coverage

One number marketing and finance could both defend

We went from defending numbers in every board meeting to making budget allocation decisions in hours.

VP of Growth

300-person B2B SaaS company with a 90-day sales cycle

Read the attribution case study

99%+ pipeline uptime

A data foundation the team stopped babysitting

Our team went from constant firefighting to barely thinking about pipeline reliability.

Head of Data

200-person mid-market SaaS team with a brittle dbt stack

Read the pipeline reliability case study

18% churn reduction in 3 weeks

A fast win tied to a real workflow

Domain Methods shipped a reverse ETL workflow in three weeks that moved the needle immediately.

Head of Product

PLG SaaS business with 15,000 active accounts

Read the data activation case study

Questions people usually have before they book

Is this a sales call?

No. The point is to understand the operating problem, pressure-test the likely root cause, and decide whether there is a sensible next step. If there is not, I will say that directly.

Do we need to choose a package before we book?

No. Bring the stuck problem: a reporting or attribution dispute, conflicting revenue definitions, a fuzzy internal ask, an unreliable data foundation, a model or workflow that needs to reach the business, or work that is already live and needs optional support. If the need is specifically AI readiness, we can also discuss the AI-Ready Data Diagnostic.

Who should join the call?

Usually the senior VP or director who owns the decision and one person who sees the data or workflow clearly. That might be a VP of Growth and RevOps lead, a Head of Data and operator, a CEO or founder plus the person carrying reporting debt every week, or — for a specialized AI-readiness question — the AI strategy owner.

What should we bring?

Bring the version of the problem you are actually arguing about now: the dashboard nobody trusts, the channel question you cannot answer, the board metric that keeps changing, or the product/revenue workflow that should move but is not trusted yet. If the question is specifically AI readiness, bring the proposed use case too. If you are asking about Managed Run + Measure, also bring what is already live, who owns it, what needs monitoring, and what would break if it drifted.

Do we need to grant system access before the call?

No. The first conversation does not require direct access to your CRM, warehouse, ad platforms, billing system, product analytics, or production tools. Screenshots, exports, schema notes, metric definitions, redacted samples, or a quick walkthrough are usually enough to decide whether there is a fit.

How is data access handled if we work together?

If the next step is a diagnostic or implementation engagement, access stays client-controlled and is scoped later through least-privilege roles, NDA, procurement, and security review. Our terms and privacy policy are legal context, not a substitute for the access-scoping conversation that follows.

What happens after the call?

If there is a fit, the next step is usually a focused diagnostic or translation sprint, a scoped service engagement, a larger foundation, activation, or model-proof build, optional support for work that is already live, or a short implementation recommendation tied to the decision you need to make. A specialized AI-Ready Data Diagnostic is available when AI readiness is the actual question. If the issue is smaller than that, I will tell you that too.